Smart Home Innovation: IOT-Based Lighting Control System Design Christine Cecylia Munthe, Arafat Febriandirza Advances in Transdisciplinary Engineering, 2024 This study involves designing a lighting control device that operates via Google Assistant and Adafruit IO using the NodeMCU ESP8266. With the rapid advancements in technology, automated control systems are essential for efficient and quick management of electronic devices. Currently, lighting systems rely heavily on manual push-button switches, and technological devices like relays and NodeMCU ESP8266 are underutilized. This study seeks to address this by creating an automated lighting control tool, with detailed documentation provided from the design phase to the result, which may serve as a reference for future applications.
Design and Construction of an Automatic Dispenser for the Visually Impaired Using Microcontroller Technology Arafat Febriandirza, Abdiel Alpriyan Gempa Alamsyah Sahuri Advances in Transdisciplinary Engineering, 2024 Water is a fundamental need for humans, comprising 70% of the human body. Dispensers are commonly used due to their convenience and hygienic nature for storing drinking water. Visually impaired individuals often face challenges in using conventional dispensers, which can lead to injuries when retrieving water. This study aims to design an automatic dispenser to assist the visually impaired, reducing the risk of injury when using dispensers. The dispenser is designed with two microcontrollers: Atmega32 and ESP8266. The ESP8266 microcontroller has a Wi-Fi feature, enabling it to connect to the internet. The dispenser uses two ultrasonic sensors to detect objects obstructing the sensor’s beam, helping control the flow of water. The system communicates between the two microcontrollers and connects to the Telegram app via Wi-Fi, allowing for remote monitoring and control. The Arduino Uno microcontroller serves as the system’s control center.
The use of Fuzzy Logic Controller and Artificial Bee Colony for optimizing adaptive SVSF in robot localization algorithm Heru Suwoyo, Muhammad Hafizd Ibnu Hajar, Prastika Indriyanti, Arafat Febriandirza Sinergi Indonesia, 2024 The objective of solving feature-based localization problems is to estimate the path of the robot referring to a given map. Thus, it is not surprising that robust estimators such as Smooth Variable Structure Filter (SVSF) are often used to handle this problem. Basically, its use is highly dependent on an accurate system model and known statistical noise. Where neither of these are available by definition. Therefore, the conventional way is not recommended and the use of an adaptive filter approach can be involved. Based on this and although only partially, Innovation Adaptive Estimation (IAE) has been considered to have a positive influence on improving the performance of the estimator. But not infrequently the solutions offered by this approach also lead to divergences due to unmapped dynamic conditions. Moreover, in this proposal, IAE is enhanced by applying Artificial Bee Colony-Tuned Fuzzy Logic. The hope is that there is quality control for the process noise covariance Q and R measurements by updating them based on the output of this ABC-Tuned FLC.
UAV-assisted heavy metal tracking in oil palm plantations: present applications and future prospects Muhammad Yudhi Rezaldi, Ambar Yoganingrum, Abdurrakhman Prasetyadi, Aang Gunawan Sutyawan, Arafat Febriandirza, Cahyo Trianggoro, Ridwan Suhud Remote Sensing Letters, 2024 Indonesia and Malaysia produce the most palm oil in the world. The world’s palm oil industry doubled in 1960–1990 and then increased steadily until 2020. However, the industry faces problems and challenges as a cause of environmental damage. Fertilizers and pesticides used in oil palm plantations and their processing waste are suspected of contaminating the environment with heavy metals. Unmanned Aerial Vehicles (UAVs) have become more widely used for environmental monitoring, particularly as a technology for tracking heavy metals on agricultural land in recent years. Previous studies claim several advantages of using this technology, such as fast operation and low cost. This study presents the state-of-the-art available UAV platforms for heavy metal tracking in the agricultural industry. Recent applications focus primarily on hyperspectral sensing and photogrammetric technique. In addition, the prospects for UAV technology to track heavy metal pollution in the palm oil industry are also analysed.
Web-Based Decision Support System of Employee Admission Using Multi Attribute Utility Theory Arafat Febriandirza, Muhammad Rizky Kurniawan, Arti Dian Nastiti Proceedings 2023 10th International Conference on Computer Control Informatics and Its Applications Exploring the Power of Data Leveraging Information to Drive Digital Innovation Ic3ina 2023, 2023 Human resources management is needed to find the right person in the right place specifically in the admitting process. The process has to meet the criteria which required will be easier with the accurate method of employee admission. To build a thriving company, they must have excellent and qualified employees. Currently, the admission system is manual using paper that will be calculated by Human Resource Department (HRD). Decision support system is needed to help HRD deciding and determining the right employee in admission process so as finding the excellent and qualified employee that meet the criteria required will be faster. In this study, Multi Attribute Utility Theory (MAUT) method was chosen in employee admitting process based on criteria required by HRD which are major, GPA, work experience, test result, and job interview result. Based on the accuracy value calculation was obtained accuracy value of 80% or as many as 12 applicants are qualified to required criteria.
Recognizing Public Satisfaction Toward Kampus Mengajar Program with Naive Bayes Ridwan Suhud, Arafat Febriandirza, Intan Permatasari, Farhan Ramadan Proceedings 2023 10th International Conference on Computer Control Informatics and Its Applications Exploring the Power of Data Leveraging Information to Drive Digital Innovation Ic3ina 2023, 2023 Merdeka Belajar Kampus Merdeka (MBKM) is a policy governed by the Minister of Education and Culture to improve college students’ capability, creativity, and innovation. Several programs are offered to realize MBKM, one is Kampus Mengajar which gives the opportunity for the participants in problem-solving, strategic development, effective, innovative, and fun learning. The teaching campus program gets different responses from the public which is conveyed on social media, various comments in the form of comments that are positive, negative, or neutral. To recognize public opinions of Kampus Mengajar’s implementation, this research conducted a sentiment analysis using A thousand and five hundred Twitter datasets containing Kampus Mengajar as a keyword. We apply pre-processing before classifying the dataset in Naive Bayes. Thus, SMOTE is carried out to overcome imbalanced data. The results showed that the level of accuracy in determining categories was 77.45% and the micro average was 77.45% in determining sentiment and had a precision level of 81.46% and a recall of 77.45%.
Digital Avatar Sub-Metaverse Modeling Using Terrestrial Photogrammetry Techniques Abdurrakhman Prasetyadi, Muhammad Yudhi Rezaldi, Cahyo Trianggoro, Arafat Febriandirza, Ridwan Suhud, Aang Gunawan, Haidaruddin Muhammad Ramdhan Proceedings 2023 10th International Conference on Computer Control Informatics and Its Applications Exploring the Power of Data Leveraging Information to Drive Digital Innovation Ic3ina 2023, 2023 In this study, we explore the metaverse, a virtual realm blending the digital and real worlds. The metaverse promises immersive experiences, and we focus on creating lifelike avatars and environments within a scientific hub called the Science and Technology Region (KST). We use a method called terrestrial photogrammetry to make 3D avatars, focusing on researchers at KST. The results show that Agisoft Metashape makes highly precise 3D models with symmetrical details, while the KIRI Engine is faster in processing. These findings suggest that we can make better avatars and environments in the metaverse, but the choice between precision and speed depends on the specific application.
Artificial Intelligence and Machine Learning Innovation in SDGs Ambar Yoganingrum, Rulina Rachmawati, Cahyo Trianggoro, Arafat Febriandirza, Koharudin Koharudin, Muhammad Yudhi Rezaldi, Abdurrakhman Prasetyadi Encyclopedia of Data Science and Machine Learning, 2022 Artificial intelligence and machine learning have become prominent fields of science and are believed to be powerful tools to achieve the Sustainable Development Goals (SDGs). Therefore, it is necessary to discuss the relationship of AI and ML to the SDGs. This chapter aims to provide information about the focus of AI and ML research on the 17 SDGs. This article finds that the amount of AI and ML research for several SDGs is very high.
Predicting New Covid-19 Outbreak Clusters among the "science Denial" Communities Budi Nugroho, Ambar Yoganingrum, Arafat Febriandirza, Abdurrakhman Prasetyadi ACM International Conference Proceeding Series, 2021 The term “herd stupidity” has recently gone viral as an innuendo for our country’s stuttering in dealing with the Covid-19 pandemic. We assessed indications of “science denial” text analysis on social media (Twitter). We developed a science denial indication dataset by utilizing the social network analysis (SNA) tool and taking geolocation data and the active cases data from the Covid-19 National Task Force regarding the distribution of clusters. We applied regression as prediction algorithms to predict areas that could become new clusters of Covid-19 spread. We tested the performance of the prediction algorithm using the mean absolute error (MAE). The experimental results show a correlation between the level of “science denial” and the formation of new clusters. The results of the prediction performance measurement show that the prediction algorithm gives acceptable results with a value of 843 MAE. This study demonstrated that Banten is the province with the highest percentage of negative sentiment and science denial text. The output provides a basis for policymakers to determine appropriate interventions in the context of controlling the Covid-19 pandemic, especially in Indonesia.
The effect of natural sounds and music on driving performance and physiological Engineering Letters, 2017
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